Project Background

This project was created for the PSY6422 Data Analysis and Visualisation module as part of the MSc in Cognitive Neuroscience and Human Neuroimaging at the University of Sheffield. See my github repository for further documentation.

Research Question

What are the trends in UK birth rates over the past 73 years?

Data Origins

I obtained a data set that contains yearly birth rates and growth rates in the UK from 1950-2023. This data was accessed through Macrotrends - a research platform that provides open source, historical and economic data.

The data was originally sourced from the United Nations World Population Prospects, and offers population estimates from 1950 to present day for 237 countries, based on information from national population censuses, registration systems and surveys.

Data Preparation

This project was developed using RMarkdown. I used packages from these libraries:

library(tidyverse)
library(rmarkdown)
library(readxl)
library(plotly)
library(gganimate)
library(gifski)

First, we load the data.

df <- read_excel("birth_rate_data.xlsx") #load data
head(df) #shows the first few lines of raw data
## # A tibble: 6 × 3
##    Year `Birth Rate` `Growth Rate`
##   <dbl>        <dbl>         <dbl>
## 1  2023         11.3       -0.0049
## 2  2022         11.3       -0.0048
## 3  2021         11.4       -0.0049
## 4  2020         11.4       -0.0048
## 5  2019         11.5       -0.0048
## 6  2018         11.5       -0.0154

Tidying Data

names(df) #returns the names of the variables in a data frame
## [1] "Year"        "Birth Rate"  "Growth Rate"

As I am focusing specifically on birth rates, the “Growth Rate” column can be removed. I did this by dropping a column using the subset() function.

drop <- c("Growth Rate")
df = df[,!(names(df) %in% drop)]

This leaves us with 2 variables (“Year” and “Birth Rate”).

names(df)
## [1] "Year"       "Birth Rate"

As variables cannot have spaces in them, I renamed “Birth Rate” as “Rate”.

names(df)[names(df) == "Birth Rate"] <- "Rate"
head(df) #shows the first few lines of processed data
## # A tibble: 6 × 2
##    Year  Rate
##   <dbl> <dbl>
## 1  2023  11.3
## 2  2022  11.3
## 3  2021  11.4
## 4  2020  11.4
## 5  2019  11.5
## 6  2018  11.5

Let’s take a look at the summary statistics to get an insight into our data.

summary(df) #shows summary statistics for processed data
##       Year           Rate      
##  Min.   :1950   Min.   :11.27  
##  1st Qu.:1968   1st Qu.:12.27  
##  Median :1986   Median :12.93  
##  Mean   :1986   Mean   :13.65  
##  3rd Qu.:2005   3rd Qu.:14.89  
##  Max.   :2023   Max.   :18.30
#save processed data
write.csv(df,"C:/Users/eva-s/OneDrive/Main/Masters/SPR/Data_Analysis_Visualisation/Another_One/df.csv", row.names=FALSE)

As we can see, the birth rates range quite a bit, from 11.27 to 18.30. We can now create a graph to visualise these changing birth rates.

Visualisation

I wanted to create a line graph to show how birth rates change over time. A line graph was the best choice for this visualisation, as it is the standard way of showing change over time - A Visual Vocabulary, Financial Times. As the data are continuous, the data points can be plotted across the graph and connected with a continuous line. This provides a clear visualisation of any peaks and troughs in the data, making it quick and easy to identify trends.

Coding the visualisation

Below shows the script for the graph. I have added comments using “#” to provide more information on specific steps along the way.

#plot year against birth rate
ggplot(
    data = df,
    mapping = aes(x = Year,
                  y = Rate)) + 
  #set y-axis limits
    ylim(
        c(10, 20))+ 
  #set birth rate as solid blue line
    geom_line(
        linewidth = 1, color = "darkblue") +
  #set x-axis breaks to 10
  scale_x_continuous(limits = c(1950, 2023), breaks = scales::breaks_width(10))+ 
    #classic theme and serif font makes the plot clearer
    theme_classic(base_family = "serif") +
  #centre allign title 
  theme(plot.title = element_text(hjust = 0.5))+
  #Assign labels, title and caption
    labs(
        x = "Year", 
        y = "Births Per 1000 People", 
        title = "UK Birth Rates", 
        caption = "Data Source: MacroTrends")

Notes

The visualisation above is clear and concise, but a little boring. So, I decided to change my graph from static to interactive. I used the plotly package, which allows users to hover over the data points for more information. In this instance, it reveals the exact year and corresponding birth rate. Instead of relying on eyeballing the data, users can now explore specific data points, allowing for a more detailed analysis.

#make graph a static object
static <- df %>% ggplot(
     data = df,
     mapping = aes(x = Year,
                   y = Rate)) + 
     ylim(
         c(10, 20))+ 
     geom_line(
         linewidth = 1, color = "darkblue") +
     scale_x_continuous(limits = c(1950, 2023), breaks = scales::breaks_width(10))+
     theme_classic(base_family = "serif") +
    theme(plot.title = element_text(hjust = 0.5))+
     labs(
         x = "Year", 
         y = "Births Per 1000 People", 
         title = "UK Birth Rates", 
         caption = "Data Source: MacroTrends")

#use ggplotly to make static object interactive
 ggplotly(static)
#save output
 ggsave('births.png')

We now have an interactive graph that shows the yearly birth rate from 1950 to 2023!

The plot saved as a static image, so I also exported it as a webpage so I could have the interactive html version. Both are available in my github repository.

Animated Plots

I wanted to challenge myself to animate the plot, as this is an advanced coding skill that could improve my visualisation. Animation in this instance is a way of emphasising change over time. Although an animated plot may not be the most scientifically efficient way of showing data, I believe that it is more engaging, as you can follow the steep peaks and troughs of the data as they occur. Additionally, it demands attention, as people are naturally drawn to moving objects. Thus, the graph fulfills its primary aim - to capture the audience’s attention. This is a vital step, as without it, the data and any trends that could be identified would go unnoticed.

ggplot(
    data = df,
    mapping = aes(x = Year,
                  y = Rate)) + 
    ylim(
        c(10, 20))+ 
    geom_line(
        linewidth = 1, color = "darkblue") +
  scale_x_continuous(limits = c(1950, 2023), breaks = scales::breaks_width(10))+ 
    theme_classic(base_family = "serif") +
theme(plot.title = element_text(hjust = 0.5))+
    labs(
        x = "Year", 
        y = "Births Per 1000 People", 
        title = "UK Birth Rates", 
        caption = "Data Source: MacroTrends")+
transition_reveal(Year)+  #reveals graph over time
  shadow_mark() #keeps rendered graph visible

Notes

After a lot of searching, I finally found a method to animate this plot. Most tutorials required the time data to be in the form of dates (e.g. DD/MM/YYYY), so it was a challenge to find one that animated an existing time-series with yearly time data. In the end, it only required a short, simple bit of code, which was quite satisfying.

Overall, I am very pleased with the final animated visualisation. Although, the animation removed the interactive hover text feature, I think it is a necessary trade-off. Users can still eyeball the data to determine the yearly rate, and there are many data sources available if they wish to know the exact details. The main point of this graph is to demonstrate trends in birth rates, so showing dynamic changes via animation seems to be the best approach.

I think the classic theme, font, and dark blue line colour make the plot appear professional. Also, the x and y axis scales are appropriate as they closely reflect the boundaries of the data, with well-spaced breaks for easy readability. Additionally, the axis labels clearly show the variables being measured, with a clear title. Finally, the eye-catching animation helps to fulfill the aims of the visualisation.

Summary

In summary, I used open source data from Macrotrends and the United Nations World Population Prospects to graph a time series of birth rates in the UK from 1950 to 2023.

My graph shows:

Disclaimer - This project was simply an exercise in data visualisation, so no statistical analyses were performed. Any trends in birth rates should be interpreted with caution and further information should be sought.

Reflection

I have learnt a lot throughout this project. As someone who had never coded before, there was a steep learning curve, but with perseverance it became more manageable and even enjoyable!

If I had more time to spend on this project, I would:

  • Attempt more unique visualisations. Due to the time constraints and strikes which limited teaching time, I felt like it was appropriate to do a simpler graph to make sure I understood everything, rather than attempting something too complex. With more time, I could branch out and explore more ways of visualising data.

  • Experiment with larger data sets, plotting multiple variables at once.

  • Learn how to incorporate hover text so I can label graphs with more information. E.g. labeling critical events that could have impacted upon birth rates, so they are always visible on the graph.